Simulation of the SMILE Soft X-ray Imager response to a southward interplanetary magnetic field turning

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)Soft X-ray Imager(SXI)will shine a spotlight on magnetopause dynamics during magnetic reconnection.We simulate an event with a southward interplanetary magnetic field turning and produce SXI count maps with a 5-minute integration time.By making assumptions about the magnetopause shape,we find the magnetopause standoff distance from the count maps and compare it with the one obtained directly from the magnetohydrodynamic(MHD)simulation.The root mean square deviations between the reconstructed and MHD standoff distances do not exceed 0.2 RE(Earth radius)and the maximal difference equals 0.24 RE during the 25-minute interval around the southward turning.

High-resolution imaging of magnetic fields of banknote anti-counterfeiting strip using fiber diamond probe

Counterfeiting of modern banknotes poses a significant challenge,prompting the use of various preventive measures.One such measure is the magnetic anti-counterfeiting strip.However,due to its inherent weak magnetic properties,visualizing its magnetic distribution has been a longstanding challenge.In this work,we introduce an innovative method by using a fiber optic diamond probe,a highly sensitive quantum sensor designed specifically for detecting extremely weak magnetic fields.We employ this probe to achieve high-resolution imaging of the magnetic fields associated with the RMB 50denomination anti-counterfeiting strip.Additionally,we conduct computer simulations by using COMSOL Multiphysics software to deduce the potential geometric characteristics and material composition of the magnetic region within the anti-counterfeiting strip.The findings and method presented in this study hold broader significance,extending the RMB 50 denomination to various denominations of the Chinese currency and other items that employ magnetic anti-counterfeiting strips.These advances have the potential to significantly improve and promote security measures in order to prevent the banknotes from being counterfeited.

Retinal vascular morphological characteristics in diabetic retinopathy: an artificial intelligence study using a transfer learning system to analyze ultra-wide field images

AIM:To investigate the morphological characteristics of retinal vessels in patients with different severity of diabetic retinopathy(DR)and in patients with or without diabetic macular edema(DME).METHODS:The 239 eyes of DR patients and 100 eyes of healthy individuals were recruited for the study.The severity of DR patients was graded as mild,moderate and severe non-proliferative diabetic retinopathy(NPDR)according to the international clinical diabetic retinopathy(ICDR)disease severity scale classification,and retinal vascular morphology was quantitatively analyzed in ultra-wide field images using RU-net and transfer learning methods.The presence of DME was determined by optical coherence tomography(OCT),and differences in vascular morphological characteristics were compared between patients with and without DME.RESULTS:Retinal vessel segmentation using RU-net and transfer learning system had an accuracy of 99%and a Dice metric of 0.76.Compared with the healthy group,the DR group had smaller vessel angles(33.68±3.01 vs 37.78±1.60),smaller fractal dimension(Df)values(1.33±0.05 vs 1.41±0.03),less vessel density(1.12±0.44 vs 2.09±0.36)and fewer vascular branches(206.1±88.8 vs 396.5±91.3),all P<0.001.As the severity of DR increased,Df values decreased,P=0.031.No significant difference between the DME and non-DME groups were observed in vascular morphological characteristics.CONCLUSION:In this study,an artificial intelligence retinal vessel segmentation system is used with 99%accuracy,thus providing with relatively satisfactory performance in the evaluation of quantitative vascular morphology.DR patients have a tendency of vascular occlusion and dropout.The presence of DME does not compromise the integral retinal vascular pattern.

Generalization ability of a CNNγ-ray localization model for radiation imaging

Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generalization capabilities of a CNN localization model with respect to theγ-ray energy and thickness of the crystal.The model maintained a high positional linearity(PL)and spatial resolution for ray energies between 59 and 1460 keV.The PL at the incident surface of the detector was 0.99,and the resolution of the central incident point source ranged between 0.52 and 1.19 mm.In modified uniform redundant array(MURA)imaging systems using a thick crystal,the CNNγ-ray localization model significantly improved the useful field-of-view(UFOV)from 60.32 to 93.44%compared to the classical centroid localization methods.Additionally,the signal-to-noise ratio of the reconstructed images increased from 0.95 to 5.63.

Integral imaging-based tabletop light field 3D display with large viewing angle

Light field 3D display technology is considered a revolutionary technology to address the critical visual fatigue issues in the existing 3D displays.Tabletop light field 3D display provides a brand-new display form that satisfies multi-user shared viewing and collaborative works,and it is poised to become a potential alternative to the traditional wall and portable display forms.However,a large radial viewing angle and correct radial perspective and parallax are still out of reach for most current tabletop light field 3D displays due to the limited amount of spatial information.To address the viewing angle and perspective issues,a novel integral imaging-based tabletop light field 3D display with a simple flat-panel structure is proposed and developed by applying a compound lens array,two spliced 8K liquid crystal display panels,and a light shaping diffuser screen.The compound lens array is designed to be composed of multiple three-piece compound lens units by employing a reverse design scheme,which greatly extends the radial viewing angle in the case of a limited amount of spatial information and balances other important 3D display parameters.The proposed display has a radial viewing angle of 68.7°in a large display size of 43.5 inches,which is larger than the conventional tabletop light field 3D displays.The radial perspective and parallax are correct,and high-resolution 3D images can be reproduced in large radial viewing positions.We envision that this proposed display opens up possibility for redefining the display forms of consumer electronics.

Single-cell volumetric imaging with light field microscopy: Advances in systems and algorithms

Single-cell volumetric imaging is essential for researching individual characteristics of cells.As a nonscanning imaging technique,lighteld microscopy(LFM)is a critical tool to achieve realtime three-dimensional imaging with the advantage of single-shot.To address the inherent limits including nonuniform resolution and block-wise artifacts,various modied LFM strategies have been developed to provide new insights into the structural and functional information of cells.This review will introduce the principle and development of LFM,discuss the improved approaches based on hardware designs and 3D reconstruction algorithms,and present the applications in single-cell imaging.

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

Development of X-ray dark-field imaging towards clinical application

Review of X-ray dark-field imaging under development is presented. Its goal is its application to clinical diagnosis of organs that have been invisible by the ordinary techniques. In order to clinically visualize tissues in detail one needs high contrast and high spatial resolution say ～50 μm. This X-ray optics comprises a Bragg asymmetric monochro-collimator and a Bragg case or a Laue case filter with capability of analyzing angle in a parallel position. Their diffraction index is 4,4,0 and the X-ray energy 35 keV (λ= 0.0354 nm). The filter has 0.6 mm thickness in the Bragg case or 1.075 mm or 2.15 mm thickness in the Laue case. Under this condition only the refracted X-rays from object can transmit through the filter while the beam that may receive absorption and/or phase change will not. Soft tissues at human joints thus taken show high contrast images so that the DFI is promising for clinical diagnosis. Preliminary X-ray absorption images of another clinical candidates of ear bones are also shown.

Optimization method of Hadamard coding plate inγ‑ray computational ghost imaging

Owing to the constraints on the fabrication ofγ-ray coding plates with many pixels,few studies have been carried out onγ-ray computational ghost imaging.Thus,the development of coding plates with fewer pixels is essential to achieveγ-ray computational ghost imaging.Based on the regional similarity between Hadamard subcoding plates,this study presents an optimization method to reduce the number of pixels of Hadamard coding plates.First,a moving distance matrix was obtained to describe the regional similarity quantitatively.Second,based on the matrix,we used two ant colony optimization arrangement algorithms to maximize the reuse of pixels in the regional similarity area and obtain new compressed coding plates.With full sampling,these two algorithms improved the pixel utilization of the coding plate,and the compression ratio values were 54.2%and 58.9%,respectively.In addition,three undersampled sequences(the Harr,Russian dolls,and cake-cutting sequences)with different sampling rates were tested and discussed.With different sampling rates,our method reduced the number of pixels of all three sequences,especially for the Russian dolls and cake-cutting sequences.Therefore,our method can reduce the number of pixels,manufacturing cost,and difficulty of the coding plate,which is beneficial for the implementation and application ofγ-ray computational ghost imaging.

Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease. Forty-eight Parkinson’s disease patients and 39 matched healthy controls underwent genotyping and 7 T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson’s disease diagnosis. We found that, in Parkinson’s disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein(SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson’s disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson’s disease.

Prospects of microwave-induced thermoacoustic imaging

Microwave-induced thermoacoustic imaging(MTAI)has advantages including the large imaging depth,high imaging resolution,high imaging contrast,and fast imaging speed.The thermoacoustic(TA)group of South China Normal University has dedicated to developing TA imaging for more than a decade and has made many breakthroughs.This review introduces these breakthroughs from two aspects including the improvement in techniques and the exploration of applications.On the technological level,there are ultrashort microwave pulse(USMP)-inducedTA imaging that can improve the imaging resolution,nonlinear thermoacoustic imaging(NTAI)that can improve the imaging contrast,polarized microwave-inducedthermoacoustic imaging(P-MTAI)that can obtain cellular-level alignment information,and more convenient and accurate handheld and multimodal probes.On the application side,the optimization and expansion have been carried out,mainly concentrating on breast and myocardial imaging.Finally,several current research directions are introduced,including the application of P-MTAI on joint imaging and research on whole-body imaging of small animals.

A novel trigger algorithm for wide-field-of-view imaging atmospheric Cherenkov technique experiments

The high-altitude detection of astronomical radiation(HADAR)experiment is a new Cherenkov observation technique with a wide field of view(FoV),aimed at observing the prompt emissions ofγ-ray bursts(GRBs).The bottleneck for this type of experiment can be found in determining how to reject the high rate of nightsky background(NSB)noise from random stars.In this work,we propose a novel method for rejecting noise,which considers the spatial properties of GRBs and the temporal characteristics of Cherenkov radiation.In space coordinates,the map between the celestial sphere and the fired photomultiplier tubes(PMTs)on the telescope's camera can be expressed as f(δ(i,j))=δ'(i',j'),which means that a limited number of PMTs is selected from one direction.On the temporal scale,a 20-ns time window was selected based on the knowledge of Cherenkov radiation.This allowed integration of the NSB for a short time interval.Consequently,the angular resolution and effective area at 100 GeV in the HADAR experiment were obtained as 0.2°and 10^(4)m^(2),respectively.This method can be applied to all wide-FoV experiments.

Study on Wave Field Characteristics and Imaging of Collapse Column in Three-Dimensional Detection with Love Channel Wave Reflected outside the Working Face

The safety accidents caused by collapse column water diversion occur frequently, which has great hidden danger to the safety production of coal mine. Limited by the space of underground, the detection of collapse column on the outside of working face has been a difficult problem. Based on this, numerical simulation and imaging research were carried out in this paper. The results indicate that when a seismic source near the roadway is excited, a part of seismic wave propagates along the roadway direction, namely direct P-wave, direct S-wave and direct Love channel wave. When the body waves and Love channel wave propagating to the outside of working face meet the interface of collapse column, the reflected Love channel wave and reflected body waves are generated. Reflection body waves and direct waves are mixed in time domain, which is difficult to identify in seismic records, while reflected Love channel wave whose amplitude is relatively strong. The reflected Love channel wave which has a large interval from other wave trains in the time domain is easily recognizable in seismic record, which makes it suitable for advanced detection of collapse column. The signal-to-noise ratio of X component is higher than that of Y component and Z component. According to the seismic records, polarization filtering was carried out to enhance the effective wave, which removed the interference waves, and the signal was migrated to get the position parameters of collapse column interface, which was basically consistent with the model position.

Reverse time migration based on normalized wavefield decomposition imaging condition

With the increasing complexity of prospecting objectives,reverse time migration( RTM) has attracted more and more attention due to its outstanding imaging quality. RTM is based on two-way wave equation,so it can avoid the limits of angle in traditional one-way wave equation migration,image reverse branch,prism waves and multi-reflected wave precisely and obtain accurate dynamic information. However,the huge demands for storage and computation as well as low frequency noises restrict its wide application. The normalized cross-correlation imaging conditions based on wave field decomposition are derived from traditional cross-correlation imaging condition,and it can eliminate the low-frequency noises effectively and improve the imaging resolution. The practical procedure includes separating source and receiver wave field into one-way components respectively,and conducting cross-correlation imaging condition to the post-separated wave field. In this way,the resolution and precision of the imaging result will be promoted greatly.

Underwater Inhomogeneous Light Field Based on Improved Convolutional Neural Net Fish Image Recognition

In this paper, artificial intelligence image recognition technology is used to improve the recognition rate of individual domestic fish and reduce the recognition time, aiming at the problem that it is difficult to easily observe the species and growth of domestic fish in the underwater non-uniform light field environment. First, starting from the image data collected by polarizing imaging technology, this paper uses subpixel convolution reconstruction to enhance the image, uses image translation and fill technology to build the family fish database, builds the Adam-Dropout-CNN (A-D-CNN) network model, and its convolution kernel size is 3 × 3. The maximum pooling was used for downsampling, and the discarding operation was added after the full connection layer to avoid the phenomenon of network overfitting. The adaptive motion estimation algorithm was used to solve the gradient sparse problem. The experiment shows that the recognition rate of A-D-CNN is 96.97% when the model is trained under the domestic fish image database, which solves the problem of low recognition rate and slow recognition speed of domestic fish in non-uniform light field.

Microlens Light Field Imaging Method Based on Bionic Vision and 3-3 Dimensional Information Transforming

his paper adopts the 3-3-2 information processing method for the capture of moving objects as its premise, and proposes a basic principle of three-dimensional (3D) imaging using biological compound eye. Traditional bionic vision is limited by the available hardware. Therefore, in this paper, the new-generation technology of microlens-array light-field camera is proposed as a potential method for the extraction of depth information from a single image. A significant characteristic of light-field imaging is that it records intensity and directional information from the lights entering the camera. Herein, a refocusing method using light-field image is proposed. By calculating the focusing cost at different depths from the object, the imaging plane of the object is determined, and a depth map is constructed based on the position of the object’s imaging plane. Compared with traditional light-field depth estimation, the depth map calculated by this method can significantly improve resolution and does not depend on the number of light-field microlenses. In addition, considering that software algorithms rely on hardware structure, this study develops an imaging hardware that is only 7 cm long based on the second-generation microlens camera’s structure, further validating its important refocusing characteristics. It thereby provides a technical foundation for 3D imaging with a single camera.

Automatic diagnosis of diabetic retinopathy using vision transformer based on wide-field optical coherence tomography angiography

Diabetic retinopathy(DR)is one of the major causes of visual impairment in adults with diabetes.Optical coherence tomography angiography(OCTA)is nowadays widely used as the golden criterion for diagnosing DR.Recently,wide-field OCTA(WF-OCTA)provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR.The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases.In this study,automatic diagnosis of DR using vision transformer was performed using WF-OCTA images(12 mm×12 mm single-scan)centered on the fovea as the dataset.WF-OCTA images were automatically classified into four classes:No DR,mild nonproliferative diabetic retinopathy(NPDR),moderate to severe NPDR,and proliferative diabetic retinopathy(PDR).The proposed method for detecting DR on the test set achieves accuracy of 99.55%,sensitivity of 99.49%,and specificity of 99.57%.The accuracy of the method for DR staging reaches up to 99.20%,which has been proven to be higher than that attained by classical convolutional neural network models.Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR.

The influence of irregular seafloor topography on the seismic wave field and migration imaging

One of the problems experienced in marine geophysical exploration is that the layered features in the migration imaging profile are blurred and the seismic energy reflected is weaker in the middle or lower parts. In this study we model the seismic wavefield records in the undulating seafloor when there is both a slight change and significant change in seafloor topography to analyze its influence on the seismic reflection data and migration imaging profiles. We compare and analyze the wave field records collected at the same point on the original and modified velocity models, and the cross-bonding resulting migration imaging profiles. The results show that whether the seismic reflection data collection is performed along the direction of the survey line or against the direction of the survey line, slight changes in the seafloor topography have little effect on the wave field records and the migration profile, while significant changes in the seafloor topography have great effect on both the wave field records and migration profile.

A novel indirect optical method for rock stress measurement using microdeformation field analysis

Stress measurement plays a crucial role in geomechanics and rock engineering,especially for the design and construction of large-scale rock projects.This paper presents a novel method,based on the traditional stress relief approach,for indirectly measuring rock stress using optical techniques.The proposed method allows for the acquisition of full-field strain evolution on the borehole’s inner wall before and after disturbance,facilitating the determination of three-dimensional(3D)stress information at multiple points within a single borehole.The study focuses on presenting the method’s theoretical framework,laboratory validation results,and equipment design conception.The theoretical framework comprises three key components:the optical imaging method of the borehole wall,the digital image correlation(DIC)method,and the stress calculation procedure.Laboratory validation tests investigate strain field distribution on the borehole wall under varying stress conditions,with stress results derived from DIC strain data.Remarkably,the optical method demonstrates better measurement accuracy during the unloading stage compared to conventional strain gauge methods.At relatively high stress levels,the optical method demonstrates a relative error of less than 7%and an absolute error within 0.5 MPa.Furthermore,a comparative analysis between the optical method and the conventional contact resistance strain gauge method highlights the optical method’s enhanced accuracy and stability,particularly during the unloading stage.The proposed optical stress measurement device represents a pioneering effort in the application of DIC technology to rock engineering,highlighting its potential to advance stress measurement techniques in the field.

Compressed sensing sparse reconstruction for coherent field imaging

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant in- fluence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.